Best Practices Summary:

• Business and technology are always changing so it is important to choose flexible solutions that allow for expanding and contracting data environments based on needs

• A tiger team of data engineers can help address urgent analytic insight needs that come up that aren’t already covered by business units

• A central data group can help ensure that data is published in a way that is reusable, with data quality, published schema, and metadata, and is built once rather than duplicated.

Case Study:

A telecommunications company sought to democratize the use of data across the company, however they faced challenges with data accessibility, trust, and understanding. It was often difficult and time consuming to access trustworthy data for analytics and data science use cases. These challenges slowed adoption of analytics preventing using the data for decision-making across areas including supply chain, marketing, and finance. 

The Director of IT Information Management set out to modernize the technology and increase the organization’s ability to leverage analytics. From a technology perspective, business data marts were developed on a cloud platform enabling them to expand and contract based on demand. It helped make data available more quickly for data scientists and other data analysts/users to solve problems. Organizationally, a tiger team of data engineers was created that applied best practices in generating reusable and high data quality supported by data modeling, published schemas, and documented metadata. This thorough approach to data publishing helped reduce duplication of data preparation activities across data sets as the data was more trusted due to the additional curation. At the same time, the tiger team was set up to have capacity for new, urgent analytics requests. The combination of quality and speed helped to increase trust and adoption. Outside the tiger team, new users in the business and functional areas could request access to data and get it quickly. The front-end experience allowed users to explore data without SQL coding skills. An example group that benefited from the changes is the credit team who were able to save time and energy normally used for sourcing and preparing data. They were able to shift their focus to more strategic credit problems than simply running the business - like optimizing offerings based on customer credit risk. 

The shift to the cloud-based platform resulted in significant cost savings for selected groups compared to the former on-premises solution while dramatically increasing accessibility and speed around use of data. Supply chain, marketing, finance, and credit teams are all set to benefit from more curated data and easily analyzed insights as the cloud data implementation progresses.